We are pleased that Dr. Reid found our article interesting and important. We also appreciate the references she provides. Unfortunately, we cannot find these references in our Universities’ libraries or through on-line searches. Three of them apparently are published in the UK; we can’t find the fourth. We anticipate that we could obtain the three that we have identified through interlibrary loan but do not have the time to do so before this letter will be printed. Our futile search points out our insularity in the US and the need for journals like
JSE that are more easily available to statistics education researchers and instructors throughout the world.

We were not familiar with these references until we read Dr. Reid’s letter. Dr. Schau developed an early version of this preliminary model with a colleague in engineering. Systems approaches are commonly used in engineering, with input, process, and output variables (although they may be called by other terms, as is the case in Biggs’ model). We applied it to statistics education because we thought it would be a useful way to think about the “bigger picture.” We included it in our article with the hope that it would prompt statistics instructors and researchers to think broadly about the factors that impact student outcomes in statistics education. There are undoubtedly a number of useful ways to model statistics teaching and learning. These two models represent related approaches, although each has unique elements.

Because our model is based on a systems approach, it has some similarities with the model by Biggs presented by Dr. Reid. Specifically, our “Learner Characteristics” factor is similar to Biggs’ “Characteristics of the Student,” as is our “Students Outcomes” factor and Biggs’ “Students’ Learning Outcomes.” Without reading his work, however, it is not clear if attitudes and beliefs are integral parts of these two factors in Biggs’ model. They are in ours.

It does not appear to us that Biggs’ model contains a factor like our “Institutional Characteristics” factor. Because of the reality of teaching in post-secondary institutions, we believe that this factor often determines how statistics courses are designed and when they are offered. We also believe, however, that the characteristics of the learners entering these courses should drive the design of these courses but often do not.

In our model, the factor of “Statistics Course Components,” unlike Biggs’ similar factor of “Course and Departmental Learning Context,” is not an input factor; instead, it is a process factor. Biggs’ process factors include two factors related to students: “Perceptions of Context” and “Approaches to Learning.” It is interesting to consider where those two student factors belong: as part of the process of teaching introductory statistics, as outcomes from these courses, or as input into them. They actually may belong in all three places. Biggs’ model makes it clear to us that we need to continue to work on our model.

Our model was not the major point of our article. It (and other models) can form the basis for future research and certainly warrant articles more fully exploring their application in statistics education. Hopefully, our article and Dr. Reid’s letter will encourage people to think about the complexity of learning and teaching introductory statistics.